Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations3240
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory430.4 KiB
Average record size in memory136.0 B

Variable types

Numeric16
Categorical1

Alerts

ProductionCost has unique values Unique
SupplierQuality has unique values Unique
DefectRate has unique values Unique
QualityScore has unique values Unique
DowntimePercentage has unique values Unique
InventoryTurnover has unique values Unique
StockoutRate has unique values Unique
WorkerProductivity has unique values Unique
EnergyConsumption has unique values Unique
EnergyEfficiency has unique values Unique
AdditiveProcessTime has unique values Unique
AdditiveMaterialCost has unique values Unique
DeliveryDelay has 521 (16.1%) zeros Zeros
MaintenanceHours has 138 (4.3%) zeros Zeros
SafetyIncidents has 293 (9.0%) zeros Zeros

Reproduction

Analysis started2025-01-21 17:51:52.350453
Analysis finished2025-01-21 17:52:11.545887
Duration19.2 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

ProductionVolume
Real number (ℝ)

Distinct862
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean548.52315
Minimum100
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:11.604626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile139
Q1322
median549
Q3775.25
95-th percentile953
Maximum999
Range899
Interquartile range (IQR)453.25

Descriptive statistics

Standard deviation262.40207
Coefficient of variation (CV)0.47837921
Kurtosis-1.2254415
Mean548.52315
Median Absolute Deviation (MAD)227
Skewness-0.0029172961
Sum1777215
Variance68854.848
MonotonicityNot monotonic
2025-01-21T23:22:11.702399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138 12
 
0.4%
198 11
 
0.3%
875 10
 
0.3%
826 9
 
0.3%
596 9
 
0.3%
229 9
 
0.3%
904 9
 
0.3%
625 9
 
0.3%
763 9
 
0.3%
747 9
 
0.3%
Other values (852) 3144
97.0%
ValueCountFrequency (%)
100 6
0.2%
101 6
0.2%
102 4
0.1%
103 2
 
0.1%
104 5
0.2%
105 1
 
< 0.1%
106 6
0.2%
107 5
0.2%
108 3
0.1%
109 5
0.2%
ValueCountFrequency (%)
999 5
0.2%
998 4
0.1%
997 3
 
0.1%
996 4
0.1%
995 3
 
0.1%
994 2
 
0.1%
993 3
 
0.1%
992 2
 
0.1%
991 5
0.2%
990 9
0.3%

ProductionCost
Real number (ℝ)

Unique 

Distinct3240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12423.018
Minimum5000.1745
Maximum19993.366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:11.799646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5000.1745
5-th percentile5759.3001
Q18728.8293
median12405.205
Q316124.462
95-th percentile19217.596
Maximum19993.366
Range14993.191
Interquartile range (IQR)7395.6331

Descriptive statistics

Standard deviation4308.0519
Coefficient of variation (CV)0.3467798
Kurtosis-1.1881488
Mean12423.018
Median Absolute Deviation (MAD)3697.8113
Skewness0.010748854
Sum40250580
Variance18559311
MonotonicityNot monotonic
2025-01-21T23:22:11.894743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13175.40378 1
 
< 0.1%
10954.88243 1
 
< 0.1%
18291.29163 1
 
< 0.1%
18435.34916 1
 
< 0.1%
9459.308064 1
 
< 0.1%
8449.906309 1
 
< 0.1%
11169.55958 1
 
< 0.1%
8607.973778 1
 
< 0.1%
15085.75765 1
 
< 0.1%
17390.97027 1
 
< 0.1%
Other values (3230) 3230
99.7%
ValueCountFrequency (%)
5000.174521 1
< 0.1%
5000.460783 1
< 0.1%
5002.020395 1
< 0.1%
5003.405573 1
< 0.1%
5008.969083 1
< 0.1%
5009.800861 1
< 0.1%
5014.151229 1
< 0.1%
5016.681413 1
< 0.1%
5020.304386 1
< 0.1%
5022.107331 1
< 0.1%
ValueCountFrequency (%)
19993.36555 1
< 0.1%
19983.38918 1
< 0.1%
19975.21267 1
< 0.1%
19967.31284 1
< 0.1%
19966.24084 1
< 0.1%
19964.42446 1
< 0.1%
19958.5685 1
< 0.1%
19957.73531 1
< 0.1%
19954.24126 1
< 0.1%
19947.89463 1
< 0.1%

SupplierQuality
Real number (ℝ)

Unique 

Distinct3240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.83329
Minimum80.00482
Maximum99.989214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:11.994640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum80.00482
5-th percentile81.039013
Q184.869219
median89.704861
Q394.789936
95-th percentile98.938308
Maximum99.989214
Range19.984394
Interquartile range (IQR)9.9207173

Descriptive statistics

Standard deviation5.7591429
Coefficient of variation (CV)0.064109228
Kurtosis-1.1878554
Mean89.83329
Median Absolute Deviation (MAD)4.9312097
Skewness0.05065569
Sum291059.86
Variance33.167727
MonotonicityNot monotonic
2025-01-21T23:22:12.113348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.64853384 1
 
< 0.1%
83.8297923 1
 
< 0.1%
81.01014942 1
 
< 0.1%
80.87762177 1
 
< 0.1%
94.40746425 1
 
< 0.1%
88.88959956 1
 
< 0.1%
85.12584751 1
 
< 0.1%
83.80826992 1
 
< 0.1%
93.39906982 1
 
< 0.1%
80.00803088 1
 
< 0.1%
Other values (3230) 3230
99.7%
ValueCountFrequency (%)
80.00482009 1
< 0.1%
80.00485869 1
< 0.1%
80.00803088 1
< 0.1%
80.01433717 1
< 0.1%
80.01579022 1
< 0.1%
80.02568851 1
< 0.1%
80.02856686 1
< 0.1%
80.04694948 1
< 0.1%
80.07312623 1
< 0.1%
80.07660581 1
< 0.1%
ValueCountFrequency (%)
99.98921362 1
< 0.1%
99.98700601 1
< 0.1%
99.98414598 1
< 0.1%
99.978094 1
< 0.1%
99.97585872 1
< 0.1%
99.97121613 1
< 0.1%
99.95344527 1
< 0.1%
99.94845878 1
< 0.1%
99.93698569 1
< 0.1%
99.93366556 1
< 0.1%

DeliveryDelay
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5589506
Minimum0
Maximum5
Zeros521
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:12.200514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7058044
Coefficient of variation (CV)0.66660309
Kurtosis-1.262321
Mean2.5589506
Median Absolute Deviation (MAD)1
Skewness-0.058797908
Sum8291
Variance2.9097686
MonotonicityNot monotonic
2025-01-21T23:22:12.274023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 569
17.6%
4 566
17.5%
5 556
17.2%
0 521
16.1%
1 516
15.9%
2 512
15.8%
ValueCountFrequency (%)
0 521
16.1%
1 516
15.9%
2 512
15.8%
3 569
17.6%
4 566
17.5%
5 556
17.2%
ValueCountFrequency (%)
5 556
17.2%
4 566
17.5%
3 569
17.6%
2 512
15.8%
1 516
15.9%
0 521
16.1%

DefectRate
Real number (ℝ)

Unique 

Distinct3240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7491164
Minimum0.50070985
Maximum4.9985294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:12.361591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.50070985
5-th percentile0.72996726
Q11.5980331
median2.7087747
Q33.9045334
95-th percentile4.7885796
Maximum4.9985294
Range4.4978196
Interquartile range (IQR)2.3065003

Descriptive statistics

Standard deviation1.3101544
Coefficient of variation (CV)0.47657291
Kurtosis-1.2197143
Mean2.7491164
Median Absolute Deviation (MAD)1.1585775
Skewness0.021624743
Sum8907.1371
Variance1.7165045
MonotonicityNot monotonic
2025-01-21T23:22:12.534444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.121492267 1
 
< 0.1%
2.422413623 1
 
< 0.1%
1.606222097 1
 
< 0.1%
3.77969981 1
 
< 0.1%
3.864994872 1
 
< 0.1%
2.288542666 1
 
< 0.1%
1.446892177 1
 
< 0.1%
2.845718631 1
 
< 0.1%
4.646289146 1
 
< 0.1%
3.378275296 1
 
< 0.1%
Other values (3230) 3230
99.7%
ValueCountFrequency (%)
0.5007098506 1
< 0.1%
0.5054162626 1
< 0.1%
0.5060013777 1
< 0.1%
0.5063819906 1
< 0.1%
0.5084273476 1
< 0.1%
0.511045639 1
< 0.1%
0.5113739742 1
< 0.1%
0.5152453159 1
< 0.1%
0.5157709859 1
< 0.1%
0.5157988291 1
< 0.1%
ValueCountFrequency (%)
4.998529424 1
< 0.1%
4.997008053 1
< 0.1%
4.994801722 1
< 0.1%
4.994375155 1
< 0.1%
4.994143168 1
< 0.1%
4.993751422 1
< 0.1%
4.993521118 1
< 0.1%
4.992381187 1
< 0.1%
4.992364201 1
< 0.1%
4.991890457 1
< 0.1%

QualityScore
Real number (ℝ)

Unique 

Distinct3240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.134272
Minimum60.010098
Maximum99.996993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:12.622922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum60.010098
5-th percentile61.822764
Q170.10342
median80.265312
Q390.353822
95-th percentile97.902362
Maximum99.996993
Range39.986895
Interquartile range (IQR)20.250402

Descriptive statistics

Standard deviation11.61175
Coefficient of variation (CV)0.14490367
Kurtosis-1.2022654
Mean80.134272
Median Absolute Deviation (MAD)10.128191
Skewness-0.030009004
Sum259635.04
Variance134.83274
MonotonicityNot monotonic
2025-01-21T23:22:12.720193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.46349433 1
 
< 0.1%
74.7253436 1
 
< 0.1%
91.14608604 1
 
< 0.1%
95.5998993 1
 
< 0.1%
97.12528744 1
 
< 0.1%
69.52488286 1
 
< 0.1%
76.95431577 1
 
< 0.1%
60.58514157 1
 
< 0.1%
95.17286637 1
 
< 0.1%
66.18288606 1
 
< 0.1%
Other values (3230) 3230
99.7%
ValueCountFrequency (%)
60.01009818 1
< 0.1%
60.01238928 1
< 0.1%
60.01320637 1
< 0.1%
60.02216738 1
< 0.1%
60.05279476 1
< 0.1%
60.05702913 1
< 0.1%
60.06899288 1
< 0.1%
60.13515387 1
< 0.1%
60.14586811 1
< 0.1%
60.16101115 1
< 0.1%
ValueCountFrequency (%)
99.99699307 1
< 0.1%
99.99220577 1
< 0.1%
99.97836269 1
< 0.1%
99.96598267 1
< 0.1%
99.9459154 1
< 0.1%
99.93075905 1
< 0.1%
99.92698952 1
< 0.1%
99.91538833 1
< 0.1%
99.90784875 1
< 0.1%
99.89282447 1
< 0.1%

MaintenanceHours
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.476543
Minimum0
Maximum23
Zeros138
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:12.807795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median12
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation6.8726837
Coefficient of variation (CV)0.59884615
Kurtosis-1.2003845
Mean11.476543
Median Absolute Deviation (MAD)6
Skewness-0.02229909
Sum37184
Variance47.233781
MonotonicityNot monotonic
2025-01-21T23:22:12.883570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
12 153
 
4.7%
19 149
 
4.6%
17 148
 
4.6%
6 145
 
4.5%
15 143
 
4.4%
2 141
 
4.4%
16 141
 
4.4%
11 140
 
4.3%
21 139
 
4.3%
3 139
 
4.3%
Other values (14) 1802
55.6%
ValueCountFrequency (%)
0 138
4.3%
1 128
4.0%
2 141
4.4%
3 139
4.3%
4 132
4.1%
5 132
4.1%
6 145
4.5%
7 136
4.2%
8 107
3.3%
9 133
4.1%
ValueCountFrequency (%)
23 122
3.8%
22 117
3.6%
21 139
4.3%
20 129
4.0%
19 149
4.6%
18 136
4.2%
17 148
4.6%
16 141
4.4%
15 143
4.4%
14 128
4.0%

DowntimePercentage
Real number (ℝ)

Unique 

Distinct3240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5013732
Minimum0.0016648394
Maximum4.9975907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:12.965655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0016648394
5-th percentile0.25229596
Q11.2645966
median2.4651505
Q33.7748609
95-th percentile4.7505163
Maximum4.9975907
Range4.9959258
Interquartile range (IQR)2.5102643

Descriptive statistics

Standard deviation1.4436845
Coefficient of variation (CV)0.57715678
Kurtosis-1.2113358
Mean2.5013732
Median Absolute Deviation (MAD)1.2545778
Skewness0.020749962
Sum8104.449
Variance2.0842249
MonotonicityNot monotonic
2025-01-21T23:22:13.056762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05234286658 1
 
< 0.1%
4.126694878 1
 
< 0.1%
3.746637568 1
 
< 0.1%
3.573952892 1
 
< 0.1%
1.403482385 1
 
< 0.1%
1.616141964 1
 
< 0.1%
1.437600993 1
 
< 0.1%
0.6442558326 1
 
< 0.1%
3.763908215 1
 
< 0.1%
0.2313825526 1
 
< 0.1%
Other values (3230) 3230
99.7%
ValueCountFrequency (%)
0.001664839426 1
< 0.1%
0.004824809218 1
< 0.1%
0.005146577827 1
< 0.1%
0.006172623014 1
< 0.1%
0.006829629375 1
< 0.1%
0.007108360155 1
< 0.1%
0.008013844593 1
< 0.1%
0.009149735748 1
< 0.1%
0.01126491306 1
< 0.1%
0.01234251493 1
< 0.1%
ValueCountFrequency (%)
4.997590657 1
< 0.1%
4.99741352 1
< 0.1%
4.996230459 1
< 0.1%
4.99611356 1
< 0.1%
4.994707853 1
< 0.1%
4.993896439 1
< 0.1%
4.991771757 1
< 0.1%
4.990795767 1
< 0.1%
4.990240109 1
< 0.1%
4.990102574 1
< 0.1%

InventoryTurnover
Real number (ℝ)

Unique 

Distinct3240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.019662
Minimum2.0016112
Maximum9.9985773
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:13.146989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.0016112
5-th percentile2.4149334
Q13.9832489
median6.0223891
Q38.050222
95-th percentile9.6142732
Maximum9.9985773
Range7.9969661
Interquartile range (IQR)4.0669732

Descriptive statistics

Standard deviation2.3297914
Coefficient of variation (CV)0.38703026
Kurtosis-1.2326618
Mean6.019662
Median Absolute Deviation (MAD)2.033449
Skewness-0.00030329174
Sum19503.705
Variance5.4279277
MonotonicityNot monotonic
2025-01-21T23:22:13.242532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.63051535 1
 
< 0.1%
9.891376893 1
 
< 0.1%
6.508096673 1
 
< 0.1%
4.842341254 1
 
< 0.1%
3.314867006 1
 
< 0.1%
5.787666819 1
 
< 0.1%
7.840907503 1
 
< 0.1%
6.989122158 1
 
< 0.1%
7.816420476 1
 
< 0.1%
8.334086018 1
 
< 0.1%
Other values (3230) 3230
99.7%
ValueCountFrequency (%)
2.001611185 1
< 0.1%
2.003592225 1
< 0.1%
2.005990474 1
< 0.1%
2.015325649 1
< 0.1%
2.015498473 1
< 0.1%
2.017965668 1
< 0.1%
2.018188957 1
< 0.1%
2.026254314 1
< 0.1%
2.027715273 1
< 0.1%
2.03268524 1
< 0.1%
ValueCountFrequency (%)
9.998577323 1
< 0.1%
9.998195821 1
< 0.1%
9.997279369 1
< 0.1%
9.991380068 1
< 0.1%
9.987960057 1
< 0.1%
9.984126527 1
< 0.1%
9.979929287 1
< 0.1%
9.979666631 1
< 0.1%
9.979010821 1
< 0.1%
9.978730684 1
< 0.1%

StockoutRate
Real number (ℝ)

Unique 

Distinct3240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.050878235
Minimum2.0517961 × 10-6
Maximum0.099997387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:13.339368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.0517961 × 10-6
5-th percentile0.0054396826
Q10.026199969
median0.051837406
Q30.075473199
95-th percentile0.094993088
Maximum0.099997387
Range0.099995335
Interquartile range (IQR)0.04927323

Descriptive statistics

Standard deviation0.02879723
Coefficient of variation (CV)0.56600293
Kurtosis-1.1961114
Mean0.050878235
Median Absolute Deviation (MAD)0.024470259
Skewness-0.051755954
Sum164.84548
Variance0.00082928046
MonotonicityNot monotonic
2025-01-21T23:22:13.439798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08132168069 1
 
< 0.1%
0.09907569405 1
 
< 0.1%
0.0590381919 1
 
< 0.1%
0.0859411527 1
 
< 0.1%
0.08952415443 1
 
< 0.1%
0.05571099121 1
 
< 0.1%
0.06820072712 1
 
< 0.1%
0.069872064 1
 
< 0.1%
0.04604808235 1
 
< 0.1%
0.04348382473 1
 
< 0.1%
Other values (3230) 3230
99.7%
ValueCountFrequency (%)
2.051796089 × 10-61
< 0.1%
6.747294866 × 10-51
< 0.1%
8.491096217 × 10-51
< 0.1%
0.0001152340795 1
< 0.1%
0.0001183693719 1
< 0.1%
0.0001557234826 1
< 0.1%
0.0001673804084 1
< 0.1%
0.0001902843225 1
< 0.1%
0.0002232553144 1
< 0.1%
0.0002514341084 1
< 0.1%
ValueCountFrequency (%)
0.09999738676 1
< 0.1%
0.09998910622 1
< 0.1%
0.0999638654 1
< 0.1%
0.09992676351 1
< 0.1%
0.09989033679 1
< 0.1%
0.09978985094 1
< 0.1%
0.09978075443 1
< 0.1%
0.09974385049 1
< 0.1%
0.09971401829 1
< 0.1%
0.09968746 1
< 0.1%

WorkerProductivity
Real number (ℝ)

Unique 

Distinct3240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.040115
Minimum80.00496
Maximum99.996786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:13.530319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum80.00496
5-th percentile80.975999
Q185.180203
median90.125743
Q395.050838
95-th percentile98.919777
Maximum99.996786
Range19.991825
Interquartile range (IQR)9.8706347

Descriptive statistics

Standard deviation5.7235995
Coefficient of variation (CV)0.063567217
Kurtosis-1.1817365
Mean90.040115
Median Absolute Deviation (MAD)4.9347865
Skewness-0.03370842
Sum291729.97
Variance32.759592
MonotonicityNot monotonic
2025-01-21T23:22:13.631209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.04237928 1
 
< 0.1%
85.5089323 1
 
< 0.1%
83.59030894 1
 
< 0.1%
81.45969945 1
 
< 0.1%
91.74687503 1
 
< 0.1%
91.37800545 1
 
< 0.1%
90.6663618 1
 
< 0.1%
99.71236536 1
 
< 0.1%
90.63088222 1
 
< 0.1%
94.4358146 1
 
< 0.1%
Other values (3230) 3230
99.7%
ValueCountFrequency (%)
80.00496047 1
< 0.1%
80.01847752 1
< 0.1%
80.02032222 1
< 0.1%
80.03211894 1
< 0.1%
80.03379549 1
< 0.1%
80.03632569 1
< 0.1%
80.03971088 1
< 0.1%
80.06072038 1
< 0.1%
80.0754926 1
< 0.1%
80.08624845 1
< 0.1%
ValueCountFrequency (%)
99.99678581 1
< 0.1%
99.99624776 1
< 0.1%
99.99182793 1
< 0.1%
99.98992225 1
< 0.1%
99.9879176 1
< 0.1%
99.98350825 1
< 0.1%
99.98253778 1
< 0.1%
99.97606863 1
< 0.1%
99.97438144 1
< 0.1%
99.97180382 1
< 0.1%

SafetyIncidents
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5916667
Minimum0
Maximum9
Zeros293
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:13.712711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8963133
Coefficient of variation (CV)0.63077604
Kurtosis-1.2476018
Mean4.5916667
Median Absolute Deviation (MAD)3
Skewness-0.013989291
Sum14877
Variance8.3886308
MonotonicityNot monotonic
2025-01-21T23:22:13.778171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9 370
11.4%
1 345
10.6%
3 342
10.6%
6 333
10.3%
8 326
10.1%
7 317
9.8%
4 307
9.5%
2 304
9.4%
5 303
9.4%
0 293
9.0%
ValueCountFrequency (%)
0 293
9.0%
1 345
10.6%
2 304
9.4%
3 342
10.6%
4 307
9.5%
5 303
9.4%
6 333
10.3%
7 317
9.8%
8 326
10.1%
9 370
11.4%
ValueCountFrequency (%)
9 370
11.4%
8 326
10.1%
7 317
9.8%
6 333
10.3%
5 303
9.4%
4 307
9.5%
3 342
10.6%
2 304
9.4%
1 345
10.6%
0 293
9.0%

EnergyConsumption
Real number (ℝ)

Unique 

Distinct3240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2988.4945
Minimum1000.7202
Maximum4997.0747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:13.858670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1000.7202
5-th percentile1198.0741
Q11988.1403
median2996.8223
Q33984.7883
95-th percentile4802.6784
Maximum4997.0747
Range3996.3546
Interquartile range (IQR)1996.648

Descriptive statistics

Standard deviation1153.4208
Coefficient of variation (CV)0.38595381
Kurtosis-1.1990902
Mean2988.4945
Median Absolute Deviation (MAD)996.44561
Skewness0.018468915
Sum9682722
Variance1330379.6
MonotonicityNot monotonic
2025-01-21T23:22:13.957548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2419.616785 1
 
< 0.1%
3917.73556 1
 
< 0.1%
3601.826447 1
 
< 0.1%
1170.106568 1
 
< 0.1%
1841.562961 1
 
< 0.1%
3889.798225 1
 
< 0.1%
3662.900056 1
 
< 0.1%
1293.315648 1
 
< 0.1%
3640.218964 1
 
< 0.1%
4722.299558 1
 
< 0.1%
Other values (3230) 3230
99.7%
ValueCountFrequency (%)
1000.720156 1
< 0.1%
1003.526908 1
< 0.1%
1004.108554 1
< 0.1%
1004.957503 1
< 0.1%
1005.44769 1
< 0.1%
1007.087805 1
< 0.1%
1007.477381 1
< 0.1%
1008.881288 1
< 0.1%
1009.012217 1
< 0.1%
1011.426011 1
< 0.1%
ValueCountFrequency (%)
4997.074741 1
< 0.1%
4996.962321 1
< 0.1%
4996.714985 1
< 0.1%
4996.330343 1
< 0.1%
4995.351045 1
< 0.1%
4992.607552 1
< 0.1%
4989.816669 1
< 0.1%
4988.069685 1
< 0.1%
4987.676 1
< 0.1%
4986.514382 1
< 0.1%

EnergyEfficiency
Real number (ℝ)

Unique 

Distinct3240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29977599
Minimum0.1002379
Maximum0.4994998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:14.050534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.1002379
5-th percentile0.11881109
Q10.20050193
median0.29747002
Q30.40265888
95-th percentile0.48009052
Maximum0.4994998
Range0.3992619
Interquartile range (IQR)0.20215695

Descriptive statistics

Standard deviation0.1163997
Coefficient of variation (CV)0.38828894
Kurtosis-1.1981919
Mean0.29977599
Median Absolute Deviation (MAD)0.10115796
Skewness0.0099409834
Sum971.27419
Variance0.01354889
MonotonicityNot monotonic
2025-01-21T23:22:14.149605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4689466237 1
 
< 0.1%
0.4878944329 1
 
< 0.1%
0.2198846278 1
 
< 0.1%
0.2248042863 1
 
< 0.1%
0.4115151664 1
 
< 0.1%
0.102184378 1
 
< 0.1%
0.3996334565 1
 
< 0.1%
0.424159372 1
 
< 0.1%
0.4605448975 1
 
< 0.1%
0.3507527213 1
 
< 0.1%
Other values (3230) 3230
99.7%
ValueCountFrequency (%)
0.1002379032 1
< 0.1%
0.1002595478 1
< 0.1%
0.1002761898 1
< 0.1%
0.100518352 1
< 0.1%
0.1005571378 1
< 0.1%
0.1006363306 1
< 0.1%
0.1007415826 1
< 0.1%
0.1010035099 1
< 0.1%
0.1010117587 1
< 0.1%
0.1013131681 1
< 0.1%
ValueCountFrequency (%)
0.4994998047 1
< 0.1%
0.4994400256 1
< 0.1%
0.4993895502 1
< 0.1%
0.4992938894 1
< 0.1%
0.4989286191 1
< 0.1%
0.4988701615 1
< 0.1%
0.4985840058 1
< 0.1%
0.4984783649 1
< 0.1%
0.4984509392 1
< 0.1%
0.4984500078 1
< 0.1%

AdditiveProcessTime
Real number (ℝ)

Unique 

Distinct3240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4720981
Minimum1.0001506
Maximum9.9997493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:14.248135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.0001506
5-th percentile1.463941
Q13.2285069
median5.4371344
Q37.7410063
95-th percentile9.5389604
Maximum9.9997493
Range8.9995987
Interquartile range (IQR)4.5124994

Descriptive statistics

Standard deviation2.598212
Coefficient of variation (CV)0.47481093
Kurtosis-1.201117
Mean5.4720981
Median Absolute Deviation (MAD)2.2512225
Skewness0.016697307
Sum17729.598
Variance6.7507055
MonotonicityNot monotonic
2025-01-21T23:22:14.335723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.55163899 1
 
< 0.1%
3.401746937 1
 
< 0.1%
2.457475792 1
 
< 0.1%
5.073130408 1
 
< 0.1%
5.7096348 1
 
< 0.1%
8.719310098 1
 
< 0.1%
7.445804769 1
 
< 0.1%
9.913515765 1
 
< 0.1%
1.708476322 1
 
< 0.1%
2.171989653 1
 
< 0.1%
Other values (3230) 3230
99.7%
ValueCountFrequency (%)
1.000150626 1
< 0.1%
1.000842345 1
< 0.1%
1.005516044 1
< 0.1%
1.01298025 1
< 0.1%
1.013000193 1
< 0.1%
1.015334452 1
< 0.1%
1.016598852 1
< 0.1%
1.018140167 1
< 0.1%
1.018222039 1
< 0.1%
1.01999659 1
< 0.1%
ValueCountFrequency (%)
9.999749326 1
< 0.1%
9.999015082 1
< 0.1%
9.991989832 1
< 0.1%
9.988817932 1
< 0.1%
9.986263161 1
< 0.1%
9.984602471 1
< 0.1%
9.982599685 1
< 0.1%
9.982328122 1
< 0.1%
9.98129149 1
< 0.1%
9.980554898 1
< 0.1%

AdditiveMaterialCost
Real number (ℝ)

Unique 

Distinct3240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299.51548
Minimum100.21114
Maximum499.98278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2025-01-21T23:22:14.507701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100.21114
5-th percentile120.62007
Q1194.92206
median299.72892
Q3403.17828
95-th percentile477.84516
Maximum499.98278
Range399.77164
Interquartile range (IQR)208.25622

Descriptive statistics

Standard deviation116.37991
Coefficient of variation (CV)0.38856057
Kurtosis-1.2497949
Mean299.51548
Median Absolute Deviation (MAD)104.05048
Skewness0.0064297832
Sum970430.15
Variance13544.282
MonotonicityNot monotonic
2025-01-21T23:22:14.606512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
236.439301 1
 
< 0.1%
172.0640532 1
 
< 0.1%
230.9106243 1
 
< 0.1%
136.9579653 1
 
< 0.1%
112.5720631 1
 
< 0.1%
280.8532633 1
 
< 0.1%
477.2160458 1
 
< 0.1%
466.8997283 1
 
< 0.1%
423.9354964 1
 
< 0.1%
351.437971 1
 
< 0.1%
Other values (3230) 3230
99.7%
ValueCountFrequency (%)
100.2111375 1
< 0.1%
100.7281144 1
< 0.1%
100.7453541 1
< 0.1%
100.8208166 1
< 0.1%
100.9560362 1
< 0.1%
100.9655018 1
< 0.1%
101.00812 1
< 0.1%
101.4033398 1
< 0.1%
101.664876 1
< 0.1%
101.8003142 1
< 0.1%
ValueCountFrequency (%)
499.9827817 1
< 0.1%
499.9295001 1
< 0.1%
499.6628433 1
< 0.1%
499.5712637 1
< 0.1%
499.3645045 1
< 0.1%
499.30844 1
< 0.1%
499.1394078 1
< 0.1%
499.1213687 1
< 0.1%
498.5429847 1
< 0.1%
498.5310032 1
< 0.1%

DefectStatus
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
1
2723 
0
517 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2723
84.0%
0 517
 
16.0%

Length

2025-01-21T23:22:14.695181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T23:22:14.757067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2723
84.0%
0 517
 
16.0%

Most occurring characters

ValueCountFrequency (%)
1 2723
84.0%
0 517
 
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2723
84.0%
0 517
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2723
84.0%
0 517
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2723
84.0%
0 517
 
16.0%

Interactions

2025-01-21T23:22:10.130034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:52.615359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:53.672374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:54.781184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:56.015055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:57.087428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:58.130001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:59.359712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:00.622176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:02.027228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:03.289725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:04.368839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:05.601733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:06.639779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:07.862823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:08.999783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:10.194543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:52.675056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:53.735035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:54.851247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:56.075947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:57.147342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:58.283680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:59.419840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:00.694445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:02.110705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:03.352131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:04.436776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:05.661342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:06.705135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:07.930929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:09.056563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:10.269546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:52.743868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:53.805364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:54.928345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:56.144286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:57.218738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:58.358965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:59.487346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:00.775007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:02.191484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:03.423975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:04.598236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-01-21T23:21:58.058976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:21:59.282473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:00.538156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:01.939697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:03.214515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:04.297392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:05.525702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:06.570047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:07.787026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:08.923871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-21T23:22:09.979041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-01-21T23:22:14.813612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AdditiveMaterialCostAdditiveProcessTimeDefectRateDefectStatusDeliveryDelayDowntimePercentageEnergyConsumptionEnergyEfficiencyInventoryTurnoverMaintenanceHoursProductionCostProductionVolumeQualityScoreSafetyIncidentsStockoutRateSupplierQualityWorkerProductivity
AdditiveMaterialCost1.0000.0130.0120.035-0.0080.0020.0250.0060.001-0.001-0.002-0.003-0.021-0.015-0.020-0.003-0.016
AdditiveProcessTime0.0131.000-0.0290.0000.028-0.008-0.005-0.0190.020-0.0010.011-0.0430.0090.0180.001-0.012-0.009
DefectRate0.012-0.0291.0000.281-0.023-0.0110.005-0.014-0.014-0.0090.014-0.019-0.0360.0120.0080.013-0.001
DefectStatus0.0350.0000.2811.0000.0000.0000.0000.0310.0000.3330.0290.1840.2320.0000.0530.0000.000
DeliveryDelay-0.0080.028-0.0230.0001.0000.0470.0070.0300.0070.0180.0170.0150.0180.006-0.0030.014-0.014
DowntimePercentage0.002-0.008-0.0110.0000.0471.0000.0030.0130.011-0.020-0.0040.019-0.0010.0040.0020.006-0.038
EnergyConsumption0.025-0.0050.0050.0000.0070.0031.000-0.0270.0270.007-0.007-0.010-0.0010.035-0.001-0.0030.015
EnergyEfficiency0.006-0.019-0.0140.0310.0300.013-0.0271.0000.026-0.026-0.0020.010-0.004-0.0130.026-0.012-0.021
InventoryTurnover0.0010.020-0.0140.0000.0070.0110.0270.0261.0000.0130.0220.007-0.001-0.0180.0260.0180.002
MaintenanceHours-0.001-0.001-0.0090.3330.018-0.0200.007-0.0260.0131.0000.007-0.004-0.0140.0090.019-0.0190.010
ProductionCost-0.0020.0110.0140.0290.017-0.004-0.007-0.0020.0220.0071.0000.029-0.002-0.0070.006-0.0240.005
ProductionVolume-0.003-0.043-0.0190.1840.0150.019-0.0100.0100.007-0.0040.0291.0000.017-0.024-0.003-0.0260.005
QualityScore-0.0210.009-0.0360.2320.018-0.001-0.001-0.004-0.001-0.014-0.0020.0171.0000.002-0.035-0.0290.005
SafetyIncidents-0.0150.0180.0120.0000.0060.0040.035-0.013-0.0180.009-0.007-0.0240.0021.0000.0320.004-0.001
StockoutRate-0.0200.0010.0080.053-0.0030.002-0.0010.0260.0260.0190.006-0.003-0.0350.0321.000-0.001-0.035
SupplierQuality-0.003-0.0120.0130.0000.0140.006-0.003-0.0120.018-0.019-0.024-0.026-0.0290.004-0.0011.000-0.018
WorkerProductivity-0.016-0.009-0.0010.000-0.014-0.0380.015-0.0210.0020.0100.0050.0050.005-0.001-0.035-0.0181.000

Missing values

2025-01-21T23:22:11.300843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-21T23:22:11.467427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ProductionVolumeProductionCostSupplierQualityDeliveryDelayDefectRateQualityScoreMaintenanceHoursDowntimePercentageInventoryTurnoverStockoutRateWorkerProductivitySafetyIncidentsEnergyConsumptionEnergyEfficiencyAdditiveProcessTimeAdditiveMaterialCostDefectStatus
020213175.40378386.64853413.12149263.46349490.0523438.6305150.08132285.04237902419.6167850.4689475.551639236.4393011
153519770.04609386.31066440.81953183.697818204.9083289.2965980.03848699.65744373915.5667130.1194859.080754353.9576311
296019060.82099782.13247204.51450490.35055012.4649235.0974860.00288792.81926423392.3853620.4963926.562827396.1894021
33705647.60603787.33596650.63852467.62869084.6924763.5776160.05533196.88701384652.4002750.1831258.097496164.1358701
42067472.22223681.98989333.86778482.72833492.7467266.8517090.06804788.31555471581.6303320.2635076.406154365.7089641
51716975.93160295.33191913.91457492.568436193.0273247.9300090.07406987.07911871238.9944210.1180217.279442171.7118041
680015889.69865099.32548634.78900090.729911103.5595613.0468890.04019291.06315883138.4311500.3339134.891669188.7277371
712017266.77994899.40148940.74360592.119681131.6048798.3809720.00970288.70556931004.1085540.2934229.333835312.5268961
87148202.67049597.30142253.18585695.17293723.4949203.6687470.05843394.29896144150.8757730.3666835.517451215.6809211
922112587.79039492.01584322.42528397.50728402.6339605.9334180.03295585.31636263023.8915550.3170715.965972364.6381760
ProductionVolumeProductionCostSupplierQualityDeliveryDelayDefectRateQualityScoreMaintenanceHoursDowntimePercentageInventoryTurnoverStockoutRateWorkerProductivitySafetyIncidentsEnergyConsumptionEnergyEfficiencyAdditiveProcessTimeAdditiveMaterialCostDefectStatus
32307376769.58725488.63610231.97646681.442254150.7289409.7219320.08910583.80930373448.4885030.3613841.325639440.0995070
32314015677.27046695.71617054.19155386.447636121.8200555.4039120.00602685.55033163160.5152080.3609472.113342454.6072950
323245911568.49830789.24913621.32776388.13940453.4044366.7870030.08932984.59445601273.4990900.2529546.894092307.3193131
323333717659.90207998.81931652.59082265.470201153.7108144.2335700.02396895.96606421897.2619000.1877333.159229435.8712141
32347468936.12564789.72744230.56146092.517908174.0670958.0668830.00779581.72257194716.1716580.1958736.679318381.3441260
323576211325.68926389.25238522.66757087.141681160.9877193.5744190.06572795.91726433288.0432420.4201864.733399299.8295770
32363355598.83798895.70143740.75127295.562997110.1781638.2952950.09769887.78084682761.3015930.1264417.234421245.5245600
323783511736.17771296.43155454.89975677.97344204.8734293.8448240.00572481.59014352000.6216480.3255305.436538206.4900101
323830213664.19621091.08978214.05766595.75559160.0716632.7832980.04261288.48852561534.7921690.2227363.776924203.7716550
323935513563.60580683.59595622.70550294.630965134.8033945.8305800.05297886.01004642610.5267360.2060992.312373324.8257540